There are a number of methods that presently exist to analyze multiparameter data. These methods all rely on some form of mathematical modeling which is used to present, rearrange or transform the data in such a way that observations may be made and conclusions drawn. Such methods include multivariate analysis, regression analysis, logrithmic transformation, etc. While such methods generally are not dependent upon the manner of data collection, in many instances those who collect data by a particular method routinely use the same form(s) of data analysis and presentation. This is especially true in the field of flow cytometry.
Flow cytometry and flow cytometers generally are described in U.S. Pat. Nos. 4,661,913, 4,284,412, and 3,826,364, and in an article by Herzenberg et al., Sci. Amer., 234:108 (1976). In principle, they operate to identify different populations of cells, typically leukocytes, in a heterogeneous cell sample, such as blood or bone marrow, by detecting multiple independent parameters on the individual cells that pass through one or more sensing regions substantially one at a time. Each sensing region essentially comprises an area illuminated by the light of a single wavelength and from which light is collected by an array of photomultiplier tubes. Each photomultiplier tube measures a separate parameter. Typically, these parameters include forward light scatter (or FLS, which is a measure of relative particle size), orthogonal light scatter (or OLS, which is a measure of relative granularity or special complexity) and fluorescence emission(s) (generally referred to as FL1, etc.).
Fluorescence may be measured from cells that incorporate a nucleic acid stain and/or may be measured from cells bearing surface or cytoplasmic markers which are labelled with monoclonal antibodies which have been conjugated directly or indirectly with fluorochromes. In the indirect method, for example, fluorescently labelled goat anti mouse antibodies are used as a second step reagent to detect the presence of the mouse derived primary monoclonal antibodies which react with the antigen of interest. Fluorochromes and stains may be referred to as fluorescent labels.
In order to identify specific cells in a heterogeneous population bearing one or more specific antigens, antibodies specific to those antigens are conjugated to fluorescent labels which have different emission spectra and, preferentially, are excitable at the same wavelength of excitation. Two labels having these properties are the fluorochromes fluorescein isothiocyanate (FITC) and phycoerythrin (PE). Other pairs of fluorochromes may be selected from the group consisting of FITC, phycoerythrin, Texas red (Molecular Probes), C-phycocyanine, allo- phycocyanine, and peridin-chlorophyll complex.
Cells reacted with the fluorescently labelled monoclonal antibodies then are examined using means to excite the fluorochromes present and to detect the fluorochrome emissions. Preferentially, such means comprise a flow cytometer wherein treated cells are passed substantially one at a time through a sensing region where light of excitation wavelength illuminates each cell and further wherein scattered light and fluorescence emitted by each cell is collected, recorded and stored in associated hardware and software. The fluorescent emission and light scatter data so recorded for each cell then may be analyzed by means of complex programs which can correlate the differential light scatter and fluorescence intensities for each of the cell types treated.
It is important that if more than one fluorescent label is used that each label have a different wavelength of emission in order that fluorescence emission from each will minimally overlap. Generally, FITC and PE meet this criteria and are used. It is preferable that the labels also be excitable at the same wavelength. This allows the cells to be in the sample to be passed through one sensing region and exposed to light of a single wavelength (e.g., from an argon laser at 488nm). In other embodiments, the flow cytometer may have more than one sensing region. In one such embodiment, a dual laser source may be used where the labels selected are not excitable at the same wavelength.
Separate detector channels within the flow cytometer are able to sense light emitted or scattered for each of the various cell parameter measurements. In a typical configuration, four or more parameters are measured (e.g., FLS, OLS, FL1 & FL2). Signals from these detectors for each cell passing through the sensing region are collected and may be stored for later data analysis by appropriately equipped recording means (e.g., a personal computer) and software (e.g., Consort 30 software or FACScan Research software, BDIS). By combining and comparing these parameters, the various leukocyte components may be identified and distinguished. U.S. Pat. No. 4,727,020 provides one example of how a flow cytometer may be used in this method to obtain leukocyte differentials from blood.
In that patent, the data collected for cells labelled by a certain fluorescent marker is presented in FIG. 2 as a histogram of log fluorescence. FIG. 3 of that patent further shows a dot plot of cells labelled by two fluorescent markers.
In U.S. Pat. No. 4,876,190, data was collected for cells labelled with a new fluorescent conjugate, PerCP. As seen in FIG. 1 of that patent, OLS is plotted versus FLS in order to discriminate between cells. The other figures in that patent are similar to those set forth in the prior mentioned patent.
In both examples, the purpose for displaying the data is to discriminate between cells of different lineages or maturational stages (e.g., between cells of myeloid versus lymphoid lineage and/or between mature and immature leukocytes). When data is presented, often one seeks to draw or define a gate around a particular population of interest, as shown in FIG. 1 of the later mentioned patent, and then to analyze separately the cells that fall within (or outside) that boundary (or gate).
Often times, however, the separation of the data is not sufficient to clearly establish this boundary such that there can be a clear distinction between cells of different types. In this case, when a boundary is defined, more (or fewer) cells will be included (or excluded) from the gate than is desired. What is needed is a method to transform the data collected in such a way that the cells of interest are more readily distinguishable from the cells of lesser or no interest without adversely effecting overall information content. The difficulty, however, is to increase the resolution within a given region of interest without sacrificing the dynamic range or losing other information contained in the data.